Institute for Clinical Evaluative Sciences, 2075 Bayview Avenue, Toronto, Ontario, Canada, M4N 3M5; CHEO Inflammatory Bowel Disease Centre, Division of Gastroenterology, Hepatology and Nutrition, Children's Hospital of Eastern Ontario, 401 Smyth Road, Ottawa, Ontario, Canada K1H 8L1; Department of Pediatrics, University of Ottawa, 451 Smyth Road, Ottawa, Ontario, Canada, K1H 8M5; Department of Epidemiology and Community Medicine, University of Ottawa, 451 Smyth Road, Ottawa, Ontario, Canada, K1H 8M5.
Institute for Clinical Evaluative Sciences, 2075 Bayview Avenue, Toronto, Ontario, Canada, M4N 3M5; Department of Paediatrics, University of Toronto, 1 King's College Circle, Toronto, Ontario, Canada, M5S 1A8; Institute of Health Policy, Management and Evaluation, 155 College Street, University of Toronto, Toronto, Ontario, Canada, M5T 3M7.
J Clin Epidemiol. 2014 Aug;67(8):887-96. doi: 10.1016/j.jclinepi.2014.02.019. Epub 2014 Apr 26.
Health administrative databases can be used to track disease incidence, outcomes, and care quality. Case validation is necessary to ensure accurate disease ascertainment using these databases. In this study, we aimed to validate adult-onset inflammatory bowel disease (IBD) identification algorithms.
We used two large cohorts of incident patients from Ontario, Canada to validate algorithms. We linked information extracted from charts to health administrative data and compared the accuracy of various algorithms. In addition, we validated an algorithm to distinguish patients with Crohn's from those with ulcerative colitis and assessed the adequate look-back period to distinguish incident from prevalent cases.
Over 5,000 algorithms were tested. The most accurate algorithm to identify patients 18 to 64 years at diagnosis was five physician contacts or hospitalizations within 4 years (sensitivity, 76.8%; specificity, 96.2%; positive predictive value (PPV), 81.4%; negative predictive value (NPV), 95.0%). In patients ≥65 years at diagnosis, adding a pharmacy claim for an IBD-related medication improved accuracy.
Patients with adult-onset incident IBD can be accurately identified from within health administrative data. The validated algorithms will be applied to administrative data to expand the Ontario Crohn's and Colitis Cohort to all patients with IBD in the province of Ontario.
健康行政数据库可用于跟踪疾病发病率、结果和医疗质量。为了确保使用这些数据库准确确定疾病，需要进行病例验证。本研究旨在验证成人发病炎症性肠病（IBD）识别算法。
我们使用来自加拿大安大略省的两个大型发病患者队列来验证算法。我们将从图表中提取的信息与健康行政数据相关联，并比较了各种算法的准确性。此外，我们验证了一种区分克罗恩病和溃疡性结肠炎患者的算法，并评估了区分发病和现患病例的适当回溯期。
测试了超过 5000 种算法。在诊断时年龄为 18 至 64 岁的患者中，最准确的识别算法是在 4 年内有 5 次医生就诊或住院（敏感性为 76.8%；特异性为 96.2%；阳性预测值（PPV）为 81.4%；阴性预测值（NPV）为 95.0%）。在诊断时年龄≥65 岁的患者中，添加 IBD 相关药物的药房配药可提高准确性。
可以从健康行政数据中准确识别出成人发病的 IBD 患者。经过验证的算法将应用于行政数据，以将安大略省克罗恩病和结肠炎队列扩展到安大略省所有 IBD 患者。
